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syndicalt/zaxy

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Zaxy turns agent work into durable, auditable memory: a hash-chained Eventloom log as the source of truth, an embedded temporal knowledge graph for reasoning (local-first, no sidec

S

MCP

syndicalt/zaxy

Added 15 June 2026

#ai #ai-agents #ai-memory #llm-tools

Overview

Zaxy provides a durable, auditable memory system for AI agents using a hash-chained Eventloom log as the source of truth. It includes an embedded temporal knowledge graph for local-first reasoning, optional Neo4j or Postgres, and cited Memory Checkout for compact context. Model-facing MCP tools enable retrieval, capture, and feedback.

Best for

Best for
Developers building agent systems that need durable, auditable memory with local-first reasoning

Use cases

  • Log agent actions in an immutable, tamper-evident event chain
  • Query a temporal knowledge graph to support agent reasoning and decision-making
  • Retrieve compact, cited context for LLM prompts via Memory Checkout

How to use

Install

pipx install zaxy-memory

Tools exposed

  • NEO4J_PASSWORD
  • MCP_ADMIN_TOKEN
  • PATHLIGHT_ACCESS_TOKEN

Tested with

Codex, Claude Code, Cursor, VS Code, Hermes Agent, LangGraph, CrewAI, AutoGen

Notes

Zaxy provides a durable, auditable memory system for AI agents using a hash-chained Eventloom log as the source of truth. It includes an embedded temporal knowledge graph for local-first reasoning, optional Neo4j or Postgres, and cited Memory Checkout for compact context. Model-facing MCP tools enable retrieval, capture, and feedback.

10 stars on GitHub. Last updated 2026-06-15. Licensed MIT.

Use cases

  • Log agent actions in an immutable, tamper-evident event chain
  • Query a temporal knowledge graph to support agent reasoning and decision-making
  • Retrieve compact, cited context for LLM prompts via Memory Checkout

Pros

  • Hash-chained log ensures audibility and integrity of agent memory
  • Local-first temporal knowledge graph eliminates need for external databases by default
  • Cited Memory Checkout reduces context footprint for efficient LLM calls

Cons

  • Requires Python environment and MCP protocol integration
  • Project is early-stage with limited community adoption (10 stars)
  • Optional external database setup (Neo4j/Postgres) may add complexity for advanced use

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Hash-chained log ensures audibility and integrity of agent memory
  • Local-first temporal knowledge graph eliminates need for external databases by default
  • Cited Memory Checkout reduces context footprint for efficient LLM calls

Cons

  • Requires Python environment and MCP protocol integration
  • Project is early-stage with limited community adoption (10 stars)
  • Optional external database setup (Neo4j/Postgres) may add complexity for advanced use
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